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Fifth IEEE International Conference on Data Mining (ICDM'05)
Modeling Multiple Time Series for Anomaly Detection
Houston, Texas
November 27-November 30
ISBN: 0-7695-2278-5
Philip K. Chan, Florida Institute of Technology
Matthew V. Mahoney, Florida Institute of Technology
Our goal is to generate comprehensible and accurate models from multiple time series for anomaly detection. The models need to produce anomaly scores in an online manner for real-life monitoring tasks. We introduce three algorithms that work in a constructed feature space and evaluate them with a real data set from the NASA shuttle program. Our offline and online evaluations indicate that our algorithms can be more accurate than two existing algorithms.
Citation:
Philip K. Chan, Matthew V. Mahoney, "Modeling Multiple Time Series for Anomaly Detection," icdm, pp.90-97, Fifth IEEE International Conference on Data Mining (ICDM'05), 2005
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